WhatAn API service that scores LLM outputs in real-time for hallucination probability using model misspecification and uncertainty quantification techniques.
SignalResearchers working on this exact problem acknowledge it is unsolved but tractable, and multiple commenters highlight that hallucination detection is the single biggest barrier to deploying AI in high-stakes domains — until solved, deep learning is limited to low-consequence tasks.
Why NowEnterprises are deploying LLMs into production at scale but getting burned by hallucinations in legal, medical, and financial contexts; regulatory pressure (EU AI Act) is creating compliance demand for output reliability scoring.
MarketEvery enterprise deploying LLMs in production (legal, healthcare, finance, customer service); TAM $5B+ as AI deployment scales; incumbents like Vectara and Galileo exist but no dominant winner yet, and current approaches are acknowledged as ill-posed.
MoatProprietary uncertainty calibration datasets built from production deployments create a flywheel — more usage generates better calibration data, improving detection accuracy.
There Will Be a Scientific Theory of Deep LearningView discussion ↗ · Article ↗ · 351 pts · April 25, 2026
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